Control Strategies for Pursuit-Evasion Under Occlusion


Minnan Zhou1
Mustafa Shaikh1
Vatsalya Chaubey1
Patrick Haggerty2
Shumon Koga3
Dimitra Panagou4
Nikolay Atanasov1

1 Existential Robotics Lab
University of California, San Diego
2General Dynamics Mission Systems
3Honda R&D 4 DASC Lab
University of Michigan


[Paper]
[arXiv]
[Code]


Abstract: This paper develops a control strategy for pursuit-evasion problems in environments with occlusions. We address the challenge of a mobile pursuer keeping a mobile evader within its field of view (FoV) despite line-of-sight obstructions. The signed distance function (SDF) of the FoV is used to formulate visibility as a control barrier function (CBF) constraint on the pursuers control inputs. Similarly, obstacle avoidance is formulated as a CBF constraint based on the SDF of the obstacle set. While the visibility and safety CBFs are Lipschitz continuous, they are not differentiable everywhere, necessitating the use of generalized gradients. To achieve non-myopic pursuit, we generate reference control trajectories via a sampling-based kinodynamic planner. The pursuer then tracks this reference via convex optimization under the CBF constraints. We validate our approach in CARLA simulations and real-world robot experiments, demonstrating successful visibility maintenance even under severe occlusions and dynamic evader movements.




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Acknowledgements

We gratefully acknowledge support from ARL DCIST CRA W911NF-17-2-0181 and all collaborating institutions. This webpage template follows the style of our llm-planning project page.